218Y_A3

For the purpose of this analysis, we chose to analyze the 94043 zip code as that encompasses the Googleplex building complex which is common destination and source of great activity in the area. This is one of the seven zip code that make up Mountain View. Below, we will explore the emissions generated by this zip code in two main categories - transportation and building emissions.

Analysis of Vehicle Emissions

To begin with, this is a map showing all trips to and from our selected zip code.

The results are quite overwhelming both in terms of quantity and distances. Not only does this support our hypothesis that this zip code would be central as it attracts thousands of people everyday but also gives us an idea of the scale of emissions very early on.

The next step is to infer the frequency of these trips but that required making an assumption that there are 261 working days a year.

Here is an aggregate of all trips in a year.

## [1] 11487297030

Combining this data with vehicle emissions data, we were able to develop an estimate for the GHG emissions these trips generate. Our methodology indicated this would be:

## [1] 3857531

Finally, here is a graph showing the trend of this value over time from 2013 to 2019 which is the focus period for this assignment. It is important to keep this constant as it allows us to draw more fair comparisons and thus, more reliable conclusions and effective recommendations.

Analysis of Building Emissions

with PG&E DATA

Although we are still only looking at that one specific zip code in Mountain View, let’s start broad.

We can see that across these four customer classes, commercial consumption stands out for both electric and gas. Interestingly, in commercial settings electricity is primary and gas is secondary while in residences, that is flipped. This makes sense when thinking about the different uses in each setting.

Looking just at the residential subspace, here is a breakdown between electricity and gas.

As we have seen before, the gas consumption is greater than electric and this graph shows this same relationship but per person. I would imagine this relationship will eventually flip in future years as more and more home appliances become electric and abandon gas usage. This is also positive in terms of the safety of homes for residents. I predict this will happen faster in more developed countries/areas.

None of these two graphs show any strong trends over time, nor positive nor negative.

Moving onto emissions, here are the same two graphs but with total CO2 emissions on the y-axis.

This graph looks quite similar to the previous one in terms of general proportions which means impact is proportional to usage at approximately the same scale. That said, this graph shows a very interesting trend over time. While both gas emissions seem to be constant, electricity emissions have been constantly dropping in residences and drop drastically in commercial setting in 2019. It is unclear why this would have happened so abruptly.

This is even more exacerbated in this second graph. While there is a steady decrease in residential electricity consumption, the drastic drop in 2019 remains an outlier.

A huge percentage of the energy use in buildings comes from heating in the Winter and cooling in the Summer. Heating systems run on gas and cooling systems run on electricity (AC). Here is a breakdown by residential and commercial, heating and cooling energy usage over time.

First is residential annual heating (gas) per resident normalized from 2013 to 2019.

There is no clear trend however it would be interesting to see if there is a correlation between consumption and colder winters, I would suspect so.

Second is the same concept but for commercial instead of residential and thus, per job instead of resident.

There seems to be more variation here and a steady and significant decrease from 2015 onward. One potential reason for this could be because the improvements tp the efficiency of heating systems over time. Alternatively, open plan offices have become more common and individual rooms have become more rare so people have been sitting more closely therefore occupying less space which minimizes the heating energy required per job.

For the third graph, we are back to residential, therefore per resident, but this time with cooling (electricity) and also normalized.

Cooling electricity per person seems to be quite consistent over the years in homes.

That said, we can already see this is not the case with commercial electricity for cooling.

Interestingly, 2015 and 2016 were significantly higher than other years. Even more so surprising is that these same years were also higher than usual for heating, also in commercial spaces.

Analysis

To begin with, let’s combine both previous parts of this analysis to compare their impact and gage the magnitude of different sources.

As we can see, emissions from vehicles are significantly higher than building emissions. I would say, building emissions correspond to approximately 25% while vehicles account for the remaining 75%.

One encouraging piece of this puzzle is that because vehicles have a much shorter life time than buildings, we replace our entire fleet of vehicles in a much shorter period than we do our buildings. This is also partially due to the difficulty in eliminating a building compared to disposing of a car (which can also be more easily recycled due to its material composition). This means we can see the impact of changes in transportation much quicker than we do for the built environment.

One of the most prominent and upcoming solutions to vehicle emissions are electric vehicles. Thus, we have chosen to explore whether there is an correlation (we expect inverse) between these two factors.

Electric Vehicles

Is EV adoption one of the many underlying factors that could contribute to your overall GHG estimates now and in the future?

The first step here is to understand the breakdown of the current car fleet.

We can see there is a clear majority of vehicles that run on gasoline, followed by gasoline hybrid. Although hard to identify, the graph shows an increase in PHEV vehicles over the years and looking at electric, while the numbers are still very low, you can see a small upwards trend.

With such small scale changes, it is often interesting to see the percentages rather than absolute values. Below is a plot showing electric vehicles as a percentage of total vehicles.

This confirms our initial analysis that there has been a consistent increase in electric vehicles however also shows us numbers are too small to already display a visible change in emissions. Overall, this looks like a promising scenario. One potential issue with the solution is providing necessary infrastructure such as charging stations which we will discuss now.

How you would try to resolve these allocation problems if you could design the GHG accounting methodology for Bay Area cities?

Per the extended producer responsibility doctrine California has continued to follow in many sustainability-related policy initiatives (read the plastic ban), we believe that the onus should be on the producers of goods (like Apple and Google’s hard goods) to internalize the negative externalities of their GHG emissions. It should not fall on the individual to consistently check up on the “sustainability hygiene” of the products they buy or consume. Individuals make choices based on market factors, utility, and personal preference. Restricting their options so that the only choices are more environmentally friendly seems like the most cohesive way of allocating GHG burden. That is to say, all options presented to consumers should be green so that they are choosing between sustainable goods (read circular economy). The resources that consumers have are minuscule compared to that of businesses and governments. Corporations need to center sustainability in order to meet the needs of the planet and people more effectively. Whether that be having a strong carpooling system or having EV charging stations at the office parking lot (or offering more remote work), companies need to make it easier for individuals to make choices that produce the least GHGs and have the least impact on the environment.

GHG emission trends in California

Taking a step further back and looking at California as a whole, here are some interesting GHG emission trends.

Let’s start with as broad as can be - here is a plot showing total GHG emission per capita.
This graph brings great news - GHG emissions per capita, while still very high, have been dropping significantly. In fact, they have changed from 11.7 in 2013 to 10.5 in 2019 (MMTCO2e).

But where is all this energy going to?

We can see how predominant Transportation is, which is in line with the previous findings comparing vehicles to buildings. After that is Industrial, followed by Electric Power which has been decreasing significantly.

Now, let’s look into the subcategories within transportation.

From this graph, we can really see how the most contributing type of transportation are Passenger Vehicles. What is tricky about this category is the common question “Who’s responsibility is this then?” and nothing really gets done. That said, as we have seen, Electric Vehicle adoption has been steadily increasing (although not significantly enough to justify this recent drop shown in the graph). This data is one more indicator of the huge potential this solution has to create a significant impact.

Finally, looking a bit more into building emissions in California over the same time period, 2013 to 2019. This source provides an interesting breakdown between housing and commercial spaces and their emissions per sqft.

It is important to note that Emissions per housing unit do not take square footage into account while commercial floor space in divided by square foot which allows for a better average across disparate levels of sustainability across different buildings.

While commercial purpose buildings show less volatility in their emissions compared to housing units, none really show improvement or consistency across these years. One potential reason for this could be that although newer buildings are reducing their emissions, there are so many less technologically advanced buildings still in use so we can’t see a difference yet.

Concluding Thoughts

Ultimately, comparing buildings and vehicles we can see vehicles correspond to a much larger portion of our consumption and emissions however, they have a shorter life time which allows for faster change and impact. Vehicles are more of an individual choice than buildings which leads to more blurry lines about who’s responsibility it is to invest/subsidize the new technology. On the other hand, decision making around buildings are more easily regulated as there are central parties such as developers.

Another interesting factor to keep in mind about buildings is that about 98% of the energy they consume are within their use phase (heating and cooling are the main examples). This means that any additional energy and money consumed during construction can be justified and will most likely be worth it. The downside of this is that we are still dealing with the consequences of older buildings.

Nonetheless, investing in more sustainable options is of utmost importance and moreover, is a time sensitive pressing issue.